Web
AuraScore 83/100

Web Agent DOM Action Grounding and Sandbox Security Assessment

Assess browser-based autonomous agent action primitives, DOM tree grounding accuracy, and isolation guardrails against web-based exploits.

Run this assessment when deploying web-grounded autonomous agents that interact with client-side DOM elements via headless browsers or accessibility trees. It validates action grounding reliability and defends against prompt injection via web page content.

Template

Role: Lead Web Automation and Agent Interaction Security Engineer with 10+ years in browser automation, DOM accessibility trees, and client-side sandbox isolation.

Context

  • Target Web Application: {{target_web_app}}
  • Browser Driver Engine: {{browser_driver_type}}
  • Action Primitives Catalog: {{action_primitive_set}}
  • Authentication Architecture: {{authentication_flow}}
  • DOM Parsing Strategy: {{dom_traversal_policy}}
  • Sandbox Isolation Tier: {{sandbox_isolation_level}}
  • Max Action Latency SLA: {{max_action_latency_ms}}

Task

Generate a rigorous DOM grounding and security assessment report analyzing the autonomous browser agent's ability to interpret web structures, execute UI actions reliably, and resist indirect injection attacks on {{target_web_app}}.

Method

  1. Analyze {{dom_traversal_policy}} (e.g., raw HTML, accessibility tree, visual bounding boxes) for semantic loss, selector instability, and dynamic shadow-DOM blind spots.
  2. Review the action primitives in {{action_primitive_set}} to verify that click, type, scroll, and select operations include pre-action validation and post-action verification checks.
  3. Evaluate the agent's vulnerability to indirect prompt injection embedded within DOM text, hidden CSS spans, image alt tags, and dynamic comment feeds on {{target_web_app}}.
  4. Assess session isolation under {{sandbox_isolation_level}} to guarantee credential boundary enforcement and prevent cross-origin state leakage via {{authentication_flow}}.
  5. Benchmark the execution overhead of DOM serialization and tool-call roundtrips against {{max_action_latency_ms}}.
  6. Inspect fallback and re-grounding routines when dynamic client-side rendering (CSR) invalidates assigned element coordinates or accessibility node IDs.
  7. Define hardened action validation wrappers that intercept dangerous browser events (e.g., file downloads, clipboard modifications, navigation to unapproved origins).

Constraints

  • MUST identify specific DOM attack surfaces relevant to {{target_web_app}}'s tech stack.
  • MUST NOT recommend exposing raw unrestricted JavaScript evaluation primitives (eval or raw console execution) to the agent.
  • Action verification protocols MUST satisfy the {{max_action_latency_ms}} constraint without sacrificing safety.
  • Recommendations MUST adhere to the security boundaries of {{sandbox_isolation_level}}.

Output format

Provide a detailed technical report formatted with:

  1. DOM Grounding Architecture Review (efficiency, token overhead, and selector stability table)
  2. Indirect Injection & Web Exploitation Threat Model (attack vectors, exploitation mechanics, risk rating)
  3. Action Primitive Hardening Blueprint (code/pseudocode wrappers for click, type, navigate)
  4. Sandbox Enforcement and Session Isolation Audit (specific browser configuration rules)

Self-review

  • Ensure all 7 variables are directly integrated into the evaluation criteria.
  • Confirm that the threat model specifically addresses indirect injection via web content rather than just system prompt attacks.
  • Check that latency recommendations remain realistic within the {{max_action_latency_ms}} threshold.
AuraScore breakdown
83/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness5/5 · Strong

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

developers
developers-web
autonomous-agents-workflows
browser-agents
dom-grounding
web-security